In Defense of Lazy Visual Grounding for Open-Vocabulary Semantic Segmentation

• URL: http://arxiv.org/abs/2408.04961v1
• Date: Fri, 9 Aug 2024 09:28:35 GMT
• Title: In Defense of Lazy Visual Grounding for Open-Vocabulary Semantic Segmentation
• Authors: Dahyun Kang, Minsu Cho,
• Abstract summary: We present lazy visual grounding, a two-stage approach of unsupervised object mask discovery followed by object grounding. Our model requires no additional training yet shows great performance on five public datasets.
• Score: 50.79940712523551
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present lazy visual grounding, a two-stage approach of unsupervised object mask discovery followed by object grounding, for open-vocabulary semantic segmentation. Plenty of the previous art casts this task as pixel-to-text classification without object-level comprehension, leveraging the image-to-text classification capability of pretrained vision-and-language models. We argue that visual objects are distinguishable without the prior text information as segmentation is essentially a vision task. Lazy visual grounding first discovers object masks covering an image with iterative Normalized cuts and then later assigns text on the discovered objects in a late interaction manner. Our model requires no additional training yet shows great performance on five public datasets: Pascal VOC, Pascal Context, COCO-object, COCO-stuff, and ADE 20K. Especially, the visually appealing segmentation results demonstrate the model capability to localize objects precisely. Paper homepage: https://cvlab.postech.ac.kr/research/lazygrounding

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